Uncover implicit associations among geochemical elements using machine learning

• Most of the major trace elements in rock and stream sediments can be reliably simulated using random forest models. • Adding feature variables can improve the simulation result of geochemical element content in the random forest model. • The method proposed in this study can assist in detecting potential errors in geochemical data. • The method proposed in this study provides a viable and reliable solution for imputing censored or missing values in geochemical data. The production of geochemical data serves diverse purposes, and a variety of analytical methods are utilized for analyzing geochemical element content. However, due to limitations in project funds, censored or missing values are common in geochemical data. This scarcity of data becomes more pronounced when dealing with large datasets. Regrettably, numerous data analysis techniques are unable to process datasets containing missing values, which presents a significant hurdle for researchers who depend on geochemical data. To address this issue, here we employed a random forest model to simulate the geochemical elements of rocks and stream sediments. By comparing and analyzing the effects of model parameters and feature variable selection on the simulation results of major and trace elements, the study found that with appropriate model parameters and variable selection, the simulation results for many elements are reliable, and the generalization performance of the random forest model is satisfactory. This research sheds light on the inherent correlations among various elements in nature, offers solutions to the challenges posed by missing values in geochemical data, and provides valuable technical support for disciplines such as geology, environmental science and soil science.